library(readxl)
Plastics <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx")
View(Plastics)
# Importing Data
GLOBAL<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet3", range = "b1:b70")
# Checking the Imported Data
View(GLOBAL)
# Creating Time Series Data
GLOBAL_ts <- ts(GLOBAL, start=c(1950), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
GLOBAL_ts
sum(is.na(GLOBAL_ts))
library(forecast)
GLOBAL_ts <- tsclean(GLOBAL_ts)
GLOBAL_ts
# Identification: Plotting the Time Series Data
plot(GLOBAL_ts)
# Estimating the appropriate model
GLOBAL_ts_model <- auto.arima(GLOBAL_ts)
GLOBAL_ts_model
# Forecasting
options(max.print=1000000)
GLOBAL_ts_forecast <- forecast (GLOBAL_ts_model, level=c(95), h=22)
plot(GLOBAL_ts_forecast)
# Importing Data
GLOBAL<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet3", range = "b1:b70")
# Checking the Imported Data
View(GLOBAL)
# Creating Time Series Data
GLOBAL_ts <- ts(GLOBAL, start=c(1950), end=c(2015), frequency=1)
# Viewing and Checking the Created Time Series Data
GLOBAL_ts
sum(is.na(GLOBAL_ts))
library(forecast)
GLOBAL_ts <- tsclean(GLOBAL_ts)
GLOBAL_ts
# Identification: Plotting the Time Series Data
plot(GLOBAL_ts)
# Estimating the appropriate model
GLOBAL_ts_model <- auto.arima(GLOBAL_ts)
GLOBAL_ts_model
# Forecasting
options(max.print=1000000)
GLOBAL_ts_forecast <- forecast (GLOBAL_ts_model, level=c(95), h=25)
plot(GLOBAL_ts_forecast)
GLOBAL_ts_forecast
# Exporting
write.table(GLOBAL_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/GLOBAL_TSA.csv", sep=",")
# Importing Data
EUROPE<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet3", range = "c1:c70")
# Checking the Imported Data
View(EUROPE)
# Creating Time Series Data
EUROPE_ts <- ts(EUROPE, start=c(1950), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
EUROPE_ts
sum(is.na(EUROPE_ts))
library(forecast)
EUROPE_ts <- tsclean(EUROPE_ts)
EUROPE_ts
# Identification: Plotting the Time Series Data
plot(EUROPE_ts)
# Estimating the appropriate model
EUROPE_ts_model <- auto.arima(EUROPE_ts)
EUROPE_ts_model
# Forecasting
options(max.print=1000000)
EUROPE_ts_forecast <- forecast (EUROPE_ts_model, level=c(95), h=22)
plot(EUROPE_ts_forecast)
EUROPE_ts_forecast
# Exporting
write.table(EUROPE_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/EUROPE_TSA.csv", sep=",")
# Importing Data
EUROPE<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet3", range = "c1:c70")
# Checking the Imported Data
View(EUROPE)
# Creating Time Series Data
EUROPE_ts <- ts(EUROPE, start=c(1950), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
EUROPE_ts
sum(is.na(EUROPE_ts))
library(forecast)
EUROPE_ts <- tsclean(EUROPE_ts)
EUROPE_ts
# Importing Data
EUROPE<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet3", range = "c1:c70")
# Checking the Imported Data
View(EUROPE)
# Creating Time Series Data
EUROPE_ts <- ts(EUROPE, start=c(1950), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
EUROPE_ts
# Identification: Plotting the Time Series Data
plot(EUROPE_ts)
# Estimating the appropriate model
EUROPE_ts_model <- auto.arima(EUROPE_ts)
EUROPE_ts_model
# Forecasting
options(max.print=1000000)
EUROPE_ts_forecast <- forecast (EUROPE_ts_model, level=c(95), h=22)
plot(EUROPE_ts_forecast)
EUROPE_ts_forecast
# Exporting
write.table(EUROPE_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/EUROPE_TSA.csv", sep=",")
# Importing Data
CHINA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "a1:a70")
library(readxl)
Plastics <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx")
View(Plastics)
# Importing Data
CHINA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "a1:a70")
library(readxl)
Plastics <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx")
View(Plastics)
# Importing Data
CHINA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "a1:a70")
# Checking the Imported Data
View(CHINA)
# Creating Time Series Data
CHINA_ts <- ts(CHINA, start=c(2006), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
CHINA_ts
sum(is.na(CHINA_ts))
library(forecast)
CHINA_ts <- tsclean(CHINA_ts)
CHINA_ts
# Identification: Plotting the Time Series Data
plot(CHINA_ts)
# Estimating the appropriate model
CHINA_ts_model <- auto.arima(CHINA_ts)
CHINA_ts_model
# Forecasting
options(max.print=1000000)
CHINA_ts_forecast <- forecast (CHINA_ts_model, level=c(95), h=22)
plot(CHINA_ts_forecast)
CHINA_ts_forecast
# Exporting
write.table(CHINA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/CHINA_TSA.csv", sep=",")
# Importing Data
JAPAN<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "b1:b70")
# Checking the Imported Data
View(JAPAN)
# Creating Time Series Data
JAPAN_ts <- ts(JAPAN, start=c(2006), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
JAPAN_ts
sum(is.na(JAPAN_ts))
library(forecast)
JAPAN_ts <- tsclean(JAPAN_ts)
JAPAN_ts
# Identification: Plotting the Time Series Data
plot(JAPAN_ts)
# Estimating the appropriate model
JAPAN_ts_model <- auto.arima(JAPAN_ts)
JAPAN_ts_model
# Forecasting
options(max.print=1000000)
JAPAN_ts_forecast <- forecast (JAPAN_ts_model, level=c(95), h=22)
plot(JAPAN_ts_forecast)
JAPAN_ts_forecast
# Exporting
write.table(JAPAN_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/JAPAN_TSA.csv", sep=",")
# Importing Data
LATIN<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "c1:c70")
# Checking the Imported Data
View(LATIN)
# Creating Time Series Data
LATIN_ts <- ts(LATIN, start=c(2006), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
LATIN_ts
sum(is.na(LATIN_ts))
library(forecast)
LATIN_ts <- tsclean(LATIN_ts)
LATIN_ts
# Identification: Plotting the Time Series Data
plot(LATIN_ts)
# Estimating the appropriate model
LATIN_ts_model <- auto.arima(LATIN_ts)
LATIN_ts_model
# Forecasting
options(max.print=1000000)
LATIN_ts_forecast <- forecast (LATIN_ts_model, level=c(95), h=22)
plot(LATIN_ts_forecast)
LATIN_ts_forecast
# Exporting
write.table(LATIN_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/LATIN_TSA.csv", sep=",")
# Importing Data
MENA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "d1:d70")
# Checking the Imported Data
View(MENA)
# Creating Time Series Data
MENA_ts <- ts(MENA, start=c(2006), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
MENA_ts
sum(is.na(MENA_ts))
library(forecast)
MENA_ts <- tsclean(MENA_ts)
MENA_ts
# Identification: Plotting the Time Series Data
plot(MENA_ts)
# Estimating the appropriate model
MENA_ts_model <- auto.arima(MENA_ts)
MENA_ts_model
# Forecasting
options(max.print=1000000)
MENA_ts_forecast <- forecast (MENA_ts_model, level=c(95), h=22)
plot(MENA_ts_forecast)
MENA_ts_forecast
# Exporting
write.table(MENA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/MENA_TSA.csv", sep=",")
# Importing Data
NAFTA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "e1:e70")
# Checking the Imported Data
View(NAFTA)
# Creating Time Series Data
NAFTA_ts <- ts(NAFTA, start=c(2006), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
NAFTA_ts
sum(is.na(NAFTA_ts))
library(forecast)
NAFTA_ts <- tsclean(NAFTA_ts)
NAFTA_ts
# Identification: Plotting the Time Series Data
plot(NAFTA_ts)
# Estimating the appropriate model
NAFTA_ts_model <- auto.arima(NAFTA_ts)
NAFTA_ts_model
# Forecasting
options(max.print=1000000)
NAFTA_ts_forecast <- forecast (NAFTA_ts_model, level=c(95), h=22)
plot(NAFTA_ts_forecast)
NAFTA_ts_forecast
# Exporting
write.table(NAFTA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/NAFTA_TSA.csv", sep=",")
# Importing Data
ASIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Plastic/Plastics.xlsx",sheet = "Sheet1", range = "f1:f70")
# Checking the Imported Data
View(ASIA)
# Creating Time Series Data
ASIA_ts <- ts(ASIA, start=c(2006), end=c(2018), frequency=1)
# Viewing and Checking the Created Time Series Data
ASIA_ts
sum(is.na(ASIA_ts))
library(forecast)
ASIA_ts <- tsclean(ASIA_ts)
ASIA_ts
# Identification: Plotting the Time Series Data
plot(ASIA_ts)
# Estimating the appropriate model
ASIA_ts_model <- auto.arima(ASIA_ts)
ASIA_ts_model
# Forecasting
options(max.print=1000000)
ASIA_ts_forecast <- forecast (ASIA_ts_model, level=c(95), h=22)
plot(ASIA_ts_forecast)
ASIA_ts_forecast
# Exporting
write.table(ASIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Plastic/ASIA_TSA.csv", sep=",")
